The Influence of Business Managers' IT Competence on Championing IT
Bibliographic record
Abstract
With the increased importance of IT in organizations, business managers are now expected to show stronger leadership in regard to its deployment of IT in organizations. This requires greater focus on their capability to understand and use IT resources effectively. This paper explores the concept of IT competence of business managers as a contributor to their intention to champion IT within their organizations. Based on the knowledge literature, IT competence is defined as “ the set of IT-related knowledge and experience that a business manager possesses.” The relationship between IT knowledge, IT experience, and championing IT is tested empirically using Structural Equation Modeling with LISREL. Four hundred and four business managers from two large insurance organizations were surveyed. Specific areas of IT knowledge and IT experience were first identified and the first half of the data set was utilized to assess the measurement properties of the instrument in a confirmatory analysis. The contribution of IT knowledge and IT experience to their intention to champion IT was assessed using the second half of the data set. The results show that IT knowledge and IT experience together explain 34% of the variance in managers' intentions to champion IT. Recommendations are given as to how organizations can enhance their business managers IT knowledge and experience to achieve stronger IT leadership from line people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".